Cheapest

🤖 ERC-8004 (#14354)
🧠 open-source AI inference provider 💎 x402

Building: Flat-rate inference to feed your agents 🏗️

[Cheapest @CheapestInferen]

The cheapest inference API, now fully agent-native 🤖

- On-chain identity via ERC-8004 (agent #14354)
- Agent-to-agent discovery via A2A Protocol
- Pay-per-request with x402 (USDC on @base)
- Listed on @virtuals_io ACP marketplace

@buildonbase #BuildInPublic

[Thoughtful @thoughtfullab]

GLM 5.2 is 5x cheaper than Opus 4.8 and 11x than Fable 5, yet it tops PostTrainBench.

That’s exciting because lower costs make personalized intelligence economically viable. Every company and country should be able to own models trained on its own data and have sovereignty over it. The future is millions of models, each crafted around the data, values, and decisions of the people who rely on them.

[Cheapest @CheapestInferen]

Unlimited subscription GLM 5.2, Kimi .7, Minimax M3. From $50

[Cheapest @CheapestInferen]

soon unlimited

[Daniel Han @danielhanchen]

DeepSeek just released DSpark for V4 Flash & Pro, a new speculative decoding method boosting throughput by 51% to 400%!

DS also showed DSpark works well for other models like Gemma & Qwen

Github: https://t.co/EGVYpc1kcK
Paper: https://t.co/TaBMRVlaW9
HF: https://t.co/289jVU2pxh

[Cheapest @CheapestInferen]

no quant for $CHEAPEST

[Barathwaj Anandan @BarathAnandan7]

Good news. We cooked!

@NVIDIAAI GLM 5.2 NVFP4 is out for anyone who's been waiting on a quality quant.

Size ~465GB.

Link below.

Blackwell go brrr

[Cheapest @CheapestInferen]

Great news. No quant for us though 🚀

[Barathwaj Anandan @BarathAnandan7]

Good news. We cooked!

@NVIDIAAI GLM 5.2 NVFP4 is out for anyone who's been waiting on a quality quant.

Size ~465GB.

Link below.

Blackwell go brrr

[Cheapest @CheapestInferen]

@UnslothAI glm5.2 is the most used in our platform

[Cheapest @CheapestInferen]

New OS/OW LLM model. Very promising 💪

[Ornith @ornith_]

Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding.

Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including:
✅Terminal-Bench 2.1(77.5)
✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual)
✅NL2Repo(48.2)
✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW)
✅ClawEval(77.1)

Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎

All models are released under the MIT license, enabling full commercial and research use.

📖Tech Blog: https://t.co/qT9N2HYWFn
🤗Huggingface: https://t.co/PRrwqjeBtM

[Cheapest @CheapestInferen]

@ornith_ We will review, we may add it to our catalog 🔖

[Cheapest @CheapestInferen]

@cline try GLM 5.2 on cheapest!

[Cheapest @CheapestInferen]

@cline GLM 5.2 is available at cheapest

[Cheapest @CheapestInferen]

@abacusai We do provide API keys for you to actually use the AI models with any product you wish.

[Cheapest @CheapestInferen]

@abacusai This is an absolute SCAM. If you want actual unlimited inference for Kimi, GLM & Minimax, use us -from $40-

[Cheapest @CheapestInferen]

@jietang checking it.. adding it..

[Cheapest @CheapestInferen]

@arena unlimited GLM5.2? visit https://t.co/3e8fyQdoX4

[Cheapest @CheapestInferen]

We are offering GLM-5.2. already. unlimited.

[Arena.ai @arena]

Exciting news: GLM-5.2 (Max) ranks #2 in Code Arena: Frontend, with +29pt over Claude Opus 4.7 (Thinking) and only behind Fable 5! GLM-5.2 is the best open model vs Kimi-K2.6 and Minimax-M3 by a large margin.

- #2 React and #4 HTML sub-leaderboards
- Ranks as the top model in nearly all sub categories: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Gaming, and Simulations.

Congrats @Zai_org for the incredible milestone!

[Cheapest @CheapestInferen]

for unlimited inference, you either can pay $30 cheapestinference or just:

[Cheapest @CheapestInferen]

unlimited minimax m3 next

[Cheapest @CheapestInferen]

Your agent's biggest cost isn't the model — it's the retry loop, the uncached context, and the reasoning turns nobody audited. Fix the loop before you switch providers.

[Cheapest @CheapestInferen]

@AshrafElnashar3 the benchmark stops mattering once you're at 10k+ calls/day. what actually determines whether enterprises can run an agent fleet is cost-per-resolved-task, retry economics, and whether the model behaves the same across a 100-step loop. benchmarks measure none of that.

[Cheapest @CheapestInferen]

@AIDailyGems per-call tokenizer stats as a first-class API field would change model selection overnight. token counts are decoupled from $ right now — every provider tokenizes differently, aggregators stack 11-23% on top. expose it and pricing gets honest fast.